Systems and methods for generating dynamic pipeline visualizations

ABSTRACT

In some embodiments, a method comprises obtaining a pipeline of operations, the pipeline of operations including a plurality of functions providing any of one or more modification operations or visualization operations for a plurality of datasets. A first dynamic visualization of the pipeline of operations at a first level of granularity is generated. A second dynamic visualization of the pipeline of operations at a second level of granularity is generated in response to user input.

CROSS REFERENCE TO RELATED APPLICATION

This application claims priority to U.S. Provisional Application No.62/749,336, filed Oct. 23, 2018, which is hereby incorporated byreference in its entirety

TECHNICAL FIELD

This disclosure pertains to systems for generating pipelinevisualizations. More specifically, this disclosure pertains togenerating dynamic pipeline visualizations.

BACKGROUND

Under some approaches, users may view pipelines that include multipleoperations on datasets. Typically, such pipelines are rendered in asingle view that present the entire pipeline to the user. However,pipelines often include many different datasets (e.g., hundreds orthousands of datasets), operations, and/or the like, which can cause thepipeline to appear cluttered or unreadable (e.g., because the visualelements are too small).

SUMMARY

A claimed solution rooted in computer technology overcomes problemsspecifically arising in the realm of computer technology. In variousimplementations, a computing system is configured to obtain a pipelineof operations. The pipeline of operations may include functions thatprovide operations (e.g., modification operations and/or orvisualization operations) for a plurality of datasets. A first dynamicvisualization of the pipeline of operations is generated at a firstlevel of granularity (e.g., a default level of granularity). Forexample, the first dynamic visualization of the pipeline of operationsmay comprise a visual representation of the entire pipeline ofoperations, but at a relatively low level of detail (e.g., showing anoverall structure of the pipeline, but not the individual datasets orfunctions). In response to user input, a second dynamic visualization ofthe pipeline of operations is generated at a second level of granularity(e.g., at a lower or higher level of granularity relative to the firstlevel of granularity). For example, a user may select a portion of thepipeline of operations in the first dynamic visualization of thepipeline of operations, which may then cause the system to zoom-in onthat portion of the pipeline of operations.

In some embodiments, when a user “zooms-in,” the resulting dynamicvisualization may present additional information that was not presentedin the prior dynamic visualization (e.g., other datasets of the pipelineof operations).

In some embodiments, a user may “zoom-out” from a portion of thepipeline of operations. For example, a user may have previously“zoomed-in” on the portion of pipeline of operations from the firstdynamic visualization of the pipeline of operations, and thensubsequently “zoomed-out” to another dynamic visualization having athird level of granularity between the first and second levels ofgranularity (e.g., showing more datasets than the first dynamicvisualization, but fewer than the second dynamic visualization).

Various embodiments of the present disclosure include systems, methods,and non-transitory computer readable media configured to obtain apipeline of operations, the pipeline of operations including a pluralityof functions providing any of one or more modification operations orvisualization operations for a plurality of datasets. A first dynamicvisualization of the pipeline of operations at a first level ofgranularity is generated. A second dynamic visualization of the pipelineof operations at a second level of granularity is generated in responseto user input.

In some embodiments, the first dynamic visualization of the pipeline ofoperations comprises a default visualization of the pipeline ofoperations.

In some embodiments, the second dynamic visualization of the pipeline ofoperations comprises a zoomed-in view of a portion of the pipeline ofoperations relative to the first dynamic visualization of the pipelineof operations.

In some embodiments, the second dynamic visualization of the pipelineincludes a representation of at least one dataset not represented in thefirst dynamic visualization of the pipeline of operations.

In some embodiments, the systems, methods, and non-transitory computerreadable media further configured to perform generating a third dynamicvisualization of the pipeline of operations at a third level ofgranularity. The system of claim 5, wherein the third dynamicvisualization of the pipeline of operations comprises a zoomed-out viewof a portion of the pipeline of operations relative to the seconddynamic visualization of the pipeline of operations, and comprises azoomed-in view of the portion of the pipeline of operations relative tothe first dynamic visualization of the pipeline of operations. Thesystem of claim 5, wherein the third dynamic visualization of thepipeline of operations includes a representation of at least one dataset not represented in the first dynamic visualization of the pipelineof operations, and does not include at least one data set represented inthe second dynamic visualization of the pipeline of operations.

In some embodiments, the systems, methods, and non-transitory computerreadable media further configured to perform obtaining a plurality ofpipeline schedules, each of the pipeline schedules indicating a time orfrequency a respective pipeline of the plurality of pipeline schedulesis to be executed; and overlaying a respective visual indicator for eachof the plurality of pipeline schedules on at least the first dynamicvisualization of the pipeline of operations at the first level ofgranularity, each of the respective visual indicators identifying acorresponding pipeline schedule of the plurality of pipeline schedules.

In some embodiments, the relative spatial relationships are maintainedbetween the first and second dynamic visualizations of the pipeline ofthe operations.

In some embodiments, a spatial distance between operations of thepipeline of operations represent a relative or absolute processing timefor those operations.

These and other features of the systems, methods, and non-transitorycomputer readable media disclosed herein, as well as the methods ofoperation and functions of the related elements of structure and thecombination of parts and economies of manufacture, will become moreapparent upon consideration of the following description and theappended claims with reference to the accompanying drawings, all ofwhich form a part of this specification, wherein like reference numeralsdesignate corresponding parts in the various figures. It is to beexpressly understood, however, that the drawings are for purposes ofillustration and description only and are not intended as a definitionof the limits of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

Certain features of various embodiments of the present technology areset forth with particularity in the appended claims. A betterunderstanding of the features and advantages of the technology will beobtained by reference to the following detailed description that setsforth illustrative embodiments, in which the principles of thetechnology are utilized, and the accompanying drawings of which:

FIG. 1 depicts a diagram of an example system for dynamically generatingand presenting pipeline visualizations according to some embodiments.

FIG. 2 depicts a diagram of an example of a dynamic pipelinevisualization system according to some embodiments.

FIG. 3A-C depicts diagrams of example dynamic visualizations of apipeline according to some embodiments.

FIG. 4 depicts a flowchart of an example of a method of generatingdynamic visualizations of a pipeline according to some embodiments.

FIG. 5 depicts a diagram of an example computer system for implementingthe features disclosed herein.

DETAILED DESCRIPTION

A claimed solution rooted in computer technology overcomes problemsspecifically arising in the realm of computer technology. In variousimplementations, a computing system is configured to obtain a pipelineof operations. The pipeline of operations may include functions thatprovide operations (e.g., modification operations and/or orvisualization operations) for a plurality of datasets. A first dynamicvisualization of the pipeline of operations is generated at a firstlevel of granularity (e.g., a default level of granularity). Forexample, the first dynamic visualization of the pipeline of operationsmay comprise a visual representation of the entire pipeline ofoperations, but at a relatively low level of detail (e.g., showing anoverall structure of the pipeline, but not the individual datasets orfunctions). In response to user input, a second dynamic visualization ofthe pipeline of operations is generated at a second level of granularity(e.g., at a lower or higher level of granularity relative to the firstlevel of granularity). For example, a user may select a portion of thepipeline of operations in the first dynamic visualization of thepipeline of operations, which may then cause the system to zoom-in onthat portion of the pipeline of operations.

In some embodiments, when a user zooms-in on a particular portion of thepipeline of the operations, the resulting dynamic visualization maypresent additional information that was not presented in the priordynamic visualization (e.g., other datasets of the pipeline ofoperations).

FIG. 1 depicts a diagram 100 of an example system for dynamicallygenerating and presenting pipeline visualizations according to someembodiments. In the example of FIG. 1, the system includes a dynamicpipeline visualization system 102, client systems 104-1 to 104-N(individually, the client system 104, collectively, the client systems104), and a communication network 106.

The dynamic pipeline visualization system 102 may function to manage(e.g., create, read, update, delete) pipelines. A pipeline may include aset of nodes with connections (e.g., “edges”) between nodes(s), wherethe output of one node is the input of a next node. A node may representone or more datasets and/or one or more functions providing operations(e.g., modification operations and/or visualization operations) to beperformed on one or more datasets. For example, a pipeline may be a datapipeline, a software deployment pipeline (e.g., continuous integrationpipeline, and/or continuous deployment pipeline), and/or the like. Insome embodiments, a pipeline may include one or more pipeline paths (or,simply, “paths”). A path may include multiple nodes and connectionsbetween nodes, and different paths may be executed sequentially, inparallel, and/or otherwise. In some embodiments, a path itself may be apipeline. Accordingly, as used herein, the term “pipeline” may refer toa collection of paths and/or particular path(s) within a largerpipeline. Additionally, the term “pipeline” may refer to the underlyingset of operations, datasets, and/or the like, and/or it may refer to arepresentation thereof (e.g., a graphical representation of nodes,connections, and/or the like). In various embodiments, functionality ofthe dynamic pipeline visualization system 102 may be performed by one ormore servers (e.g., a cloud-based server) and/or other computing devices(e.g., desktop computers, laptop computers, mobile devices, and/or thelike).

In some embodiments, the dynamic pipeline visualization system 102 mayfunction to generate and/or present dynamic visualizations (e.g.,graphical user interfaces) of pipelines. For example, a pipeline mayinclude many nodes and/or many datasets, and it may not be possible topresent all of the nodes and/or datasets of the pipeline in a singleview (e.g., a graphical user interface screen) such that the informationis useable (e.g., readable). Accordingly, the dynamic pipelinevisualization system 102 may present a pipeline in a view that is usable(e.g., readable) and that may be dynamically updated (e.g., in real-timeand/or based on user input).

In some embodiments, the dynamic pipeline visualization system 102 mayfunction to dynamically update pipeline visualizations to present apipeline, and/or or portions thereof, at different levels ofgranularity. Levels of granularity may refer to a relative or absolutepresentation scale of a graphical representation of a pipeline (or,“pipeline visualization”), a relative or absolute resolution of apipeline visualization, and/or a relative or absolute amount ofinformation presented in a pipeline visualization. For example, thedynamic pipeline visualization system 102 may display a visualization ofan entire pipeline, albeit at a relatively low-level of granularity(e.g., enough detail to identify an overall structure of the pipeline,but not enough detail to identify all of the datasets or operations ofthe pipeline). The dynamic pipeline visualization system 102 may updatea pipeline visualization in real-time (e.g., in response to user input)to generate a visualization having a higher-level of granularity (e.g.,by zooming-in) and/or a lower-level of granularity (e.g., byzooming-out).

In some embodiments, the dynamic pipeline visualization system 102 mayfunction to present pipeline schedules. A pipeline schedule may be aschedule for a when a pipeline, and/or one or more paths within apipeline, are scheduled to be executed. A pipeline schedule may refer toa specific date/time, a frequency (e.g., daily at 6:00 AM), and/or thelike. For example, a pipeline schedule may indicate that one path isscheduled to be executed (e.g., to build a binary or software object,compile and/or deploy code, and/or the like) daily at 6:00 AM, anotherpath is scheduled to be executed weekly on Monday at 9:00 AM, and/or thelike. In some embodiments, the dynamic pipeline visualization system 102may graphically present various pipeline schedules onto a pipeline. Forexample, the dynamic pipeline visualization system 102 may indicatescheduled pipelines using different colors (e.g., red, white, blue) fordifferent schedules, and also indicate a schedule for each of thosedifferent pipeline schedules (e.g., via a mouseover on the individualpath segments and/or nodes). The graphical representation of pipelineschedules may, for example, allow users to easily identify existingpipelines instead of creating new pipelines, which may becomputationally expensive.

The client systems 104 may function to present pipelines and/or interactwith pipelines. For example, a client system 104 may display one or moreGUIs including various dynamic pipeline visualizations. In someembodiments, functionality of the client systems 104 may be performed byone or desktop computers, laptop computers, mobiles devices, serversand/or other computing devices.

The communications network 106 may represent one or more computernetworks (e.g., LAN, WAN, or the like) or other transmission mediums.The communication network 110 may provide communication between thedynamic pipeline visualization system 102 and client systems 104, and/orother systems/engines described herein. In some embodiments, thecommunication network 106 includes one or more computing devices,routers, cables, buses, and/or other network topologies (e.g., mesh, andthe like). In some embodiments, the communication network 106 may bewired and/or wireless. In various embodiments, the communication network106 may include the Internet, one or more wide area networks (WANs) orlocal area networks (LANs), one or more networks that may be public,private, IP-based, non-IP based, and so forth.

FIG. 2 depicts a diagram 200 of an example of a dynamic pipelinevisualization system 102 according to some embodiments. In the exampleof FIG. 2, the dynamic pipeline visualization system 102 includes amanagement engine 202, a pipeline interaction engine 204, a pipelineschedule engine 206, a pipeline processing engine 208, a communicationengine 210, and a dynamic pipeline visualization system datastore 212.

The management engine 202 may function to manage (e.g., create, read,update, delete, or otherwise access) pipeline(s) 220 stored in thedynamic pipeline visualization system datastore 212, pipeline nodeinformation 222 stored in the dynamic pipeline visualization systemdatastore 212, contextual information 224 stored in the dynamic pipelinevisualization system datastore 212, pipeline schedule(s) 226, and/orother data stored in the dynamic pipeline visualization system datastore212 and/or other datastores. The management engine 202 may perform anyof these operations manually (e.g., by a user interacting with a GUI)and/or automatically (e.g., triggered by one or more of the engines204-210). Like other engines described herein, some or all of thefunctionality of the management engine 202 may be included in one ormore other engines (e.g., engines 204-210).

The pipeline interaction engine 204 may function to obtain one or morepipelines 220. A pipeline 220 may include multiple connected nodes, andeach of the nodes may be associated with pipeline node information 222.Pipeline node information 222 may include functions and/or datasets,and/or references thereto. Accordingly, a pipeline 220 may provide oneor more functions for providing operations (e.g., modificationoperations or visualization operations) on a plurality of datasets. Anexample pipeline is shown in FIGS. 3A-C.

In some embodiments, a pipeline 220 may include one or more pipelines ofoperations. A pipeline of operations may include one or more functions.For example, the functions may provide one or more modificationoperations and/or visualization operations that may be performed on oneor more portions of data. Modification operations may include preparingdata (e.g., cleaning data, normalizing data, filtering data), buildingbinaries and/or objects, software deployment functions (e.g., continuousintegration functions, continuous deployment functions), and/or thelike. Visualization functions may include generating graphicalrepresentations (e.g., plots, tables, graphs, maps, charts) of dataand/or other graphical user interfaces. The pipeline of operations maydefine an order in which the functions are applied to data. The pipelineof operations may include a linear pipeline or a branching pipeline.

In some embodiments, a function may refer to one or more groupings ofcode that perform one or more specific operations on data. A set offunctions may refer to a grouping of one or more functions. Operationson data may include processes that modify the data (e.g., change thedata, create new data based on the data, delete the data, combine thedata with other data), processes that visualize the data (e.g., in aplot, in a table, in a chart, in a map), and/or other operations of thedata. In some embodiments, functions may be specific to the data (e.g.,the type of data accessed), the user (e.g., the type of user, user'sprivilege level), and/or other information. The functions may beselected by users to generate one or more pipelines of operations on thedata.

In some embodiments, the pipeline interaction engine 204 functions todynamically generate a pipeline 220. For example, a pipeline 220 may beupdated/modified when users select a new function for inclusion in thepipeline 220. The pipeline may be updated/modified when users remove afunction from the pipeline. The pipeline of operations may beupdated/modified when users change the ordering of functions within thepipeline.

In some embodiments, the pipeline interaction engine 204 functions todisplay pipelines 220. For example, the functions selected by users maybe displayed within a user interface, with the functions listed in agiven order based on the users' selections. Users may use the displayedpipeline to make changes to the pipeline and/or the displayed functions.Users may use the displayed pipeline to add a new function (e.g., to thebeginning, to the end, or within the pipeline), remove an existingfunction from the pipeline, or rearrange the order of the functionswithin the pipeline. Users may use the displayed pipeline to viewinformation regarding the functions within the pipeline (e.g.,properties of the function, arguments/variables of the functions, codeof the functions, data transformations by the functions, and/or otherpipeline node information 222) and/or to modify the code of thefunctions within the pipelines.

In some embodiments, the pipeline interaction engine 204 functions togenerate dynamic visualizations of pipelines 220. Dynamic visualizationsof pipelines 220 may be associated with levels of granularity. Forexample, the pipeline interaction engine 204 may generate a dynamicvisualization of a pipeline 220 and receive one or more user inputsindicating one or more zoom-in (or, drill-down) actions and/or one ormore zoom-out (or, drill-up) actions. In some embodiments, the pipelineinteraction engine 204 updates dynamic visualizations in real-time,thereby allowing a user to interact (e.g., zoom-in, zoom-out) with apipeline 220 in real-time. For example, a dynamic visualization of apipeline 220 may comprise a two-dimensional representation of a pipeline220 (e.g., as shown in FIG. 3A), and the pipeline interaction engine 204may, in response to user input, zoom-in on various portions of thepipeline to provide higher levels of granularity, and/or zoom-out toprovide lower levels of granularity. In some embodiments, zooming-in maycause the dynamic visualization to display additional information of thepipeline (e.g., some or all node information 220 associated with thatportion of the pipeline 220). Zooming-out may cause the dynamicvisualization to display less information of the pipeline (e.g., less ornone of the node information 222 associated with that portion of thepipeline 220).

Although 2D dynamic visualization of pipelines 220 are primarilydiscussed herein, it will be appreciated that dynamic visualization mayalso include three-dimensional (3D) representations of pipelines 220.Accordingly, the pipeline interaction engine 220 may update the dynamicvisualizations to rotate/display dynamic visualizations of pipelineswithin 360 degrees. The pipeline interaction engine 204 may also switchbetween 2D and 3D views (e.g., based on user input).

In some embodiments, the pipeline interaction engine 204 functions toupdate dynamic visualizations while maintaining one or more spatialrelationship properties of the pipeline 220. For example, nodes of apipeline may be spaced within various distances of each other in adynamic visualization. As the pipeline interaction engine 204 zooms-inand/or zooms-out, the absolute distance may change, but the relativedistance may stay the same. For example, a default dynamic visualizationof a pipeline 220 may show two nodes that are 1″ apart (and connected byan edge), another node that is 1.5″ from the second node (and connectedby another edge), and so forth. If a pipeline interaction engine 204zooms-in (e.g., a 150% zoom) on a portion of the pipeline with thosenodes, the pipeline processing engine 204 may update the dynamicvisualization while maintaining the same relative distance based on thezoom level (e.g., 150%).

In some embodiments, the spatial properties of a dynamic visualizationof a pipeline 220 may indicate a processing time (e.g., build time,execution time) and/or other computing resource requirements (e.g.,memory requirements, processing power requirements, bandwidthrequirements) associated with the pipeline, and/or portions thereof. Forexample, the length of a connection between two nodes may indicate aprocessing time to perform that portion of the pipeline. Additionalspatial properties (e.g., size of nodes, width of connections) mayindicate the same or different computing requirements, and/or indicatenode information 222. For example, a size of node may indicate a numberof associated datasets and/or an amount of data in the associateddatasets.

In some embodiments, the pipeline interaction engine 204 determines atype of information (e.g., functions, datasets) and/or an amount ofinformation to present for various levels of granularity. For example,the pipeline interaction engine 204 may determine the type ofinformation and the amount of information to present for a particulardynamic visualization based on contextual information 224. Contextualinformation 224 may be stored as metadata. Contextual information 224may include usage history (e.g., how often a pipeline and/or pipelinepaths are executed overall and/or by particular users or groups ofusers, how often particular datasets are used/interacted with overalland/or by particular users or groups of users), user favorites (e.g.,bookmarked pipelines, paths, datasets, functions), desired objectives(e.g., building a particular object, executing a particular pipeline orpath), user privileges, and/or the like. For example, a user mayroutinely interact with particular datasets, while rarely interactingwithout other datasets. Accordingly, the pipeline interaction engine 204may present the datasets that the user routinely uses even if thecurrent dynamic visualization of the pipeline is at a relatively lowlevel of granularity (e.g., a default zoom level), while the pipelineinteraction engine 204 may only present the other datasets at relativelyhigh levels of granularity (e.g., a 300% zoom level). Accordingly, twodifferent users may view the same pipeline 220 but be presented withdifferent dynamic visualizations presenting different information (e.g.,node information 222) associated with that pipeline. Similarly, when thepipeline interaction engine 204 zooms-in and/or zooms-out, the twodifferent users may be presented with different information even thoughthey are viewing the same pipeline at the same zoom level.

In some embodiments, the pipeline interaction engine 204 determinesspatial relationships of a pipeline. The pipeline interaction engine 204may determine spatial relationships based on computing resourcerequirements, as discussed elsewhere herein, and/or based on contextualinformation 224. For example, different users may have different usersprivileges. A user with higher privileges (e.g., a power user, projectmanager, administrator) may be given access to more computing resources,which may result in faster processing times for pipeline executions,which may be represented by relatively shorter connections betweenparticular nodes of a pipeline. In some embodiments, if a privilegelevel changes, and/or other contextual information 224 changes, thepipeline interaction engine 204 may update the dynamic visualization ofthe pipeline accordingly (e.g., in real-time). For example, if a user isviewing a dynamic visualization a pipeline 220, and the user'sprivileges get increased (e.g., by project manager), particular nodeconnections may become shorter. In another example, whenrequirements/dependencies changes, that may also be dynamicallyreflected by shortening or lengthening connections, and/or changingother spatial properties.

The pipeline schedule engine 206 may function to generate pipelineschedules 226. A pipeline schedule 226 may indicate one or morepipelines/paths to execute and when to execute them. For example, aparticular path may be scheduled for execution daily at 6 AM, whileanother path may be scheduled for execution weekly on Monday at LOAM.The pipeline schedule engine 206 may generate pipeline schedules 226manually (e.g., in response to user input) and/or automatically. Forexample, a user may specify one or more pipelines 220 (e.g., using oneor more pipeline identifiers associated with the one or more pipeline220), and/or one or more paths (e.g., via one or more path identifiers),and a specific date/time and/or frequency that they should be executed.

In some embodiments, the pipeline schedule engine 206 may function topresent pipeline schedules 226. For example, the pipeline scheduleengine 206 may apply visual indicators (e.g., graphical overlays) on toa dynamic visualization of a pipeline 220. Visual indicators may includehighlighting and providing/changing colors of nodes and/or connectionswithin a dynamic visualization of a pipeline 220. For example, theconnections of a first scheduled path may be blue, the connections of asecond scheduled path may be red, and/or the like. The scheduleinformation (e.g., execution time, frequency) may be presented in thedynamic visualization of the pipeline 220 (e.g., in response to userinput, such as a mouseover).

In some embodiments, the pipeline schedule engine 206 may presentpipeline schedules 226 based on context. For example, a user may have adesired objective (e.g., build various objects, view various datasets),and in order to ensure they have the most recent data (e.g., objects,datasets), the pipeline schedule engine 206 may present one or morepipeline schedules to obtain the desired objective. The presentedpipeline schedules may also represent the most computationally efficientmeans of obtaining the desired objective.

The pipeline processing engine 208 may function to execute and/orinterpret pipelines 220 and/or portions thereof. For example, thepipeline processing engine 208 may perform the functions on the datasetsas indicated by the pipeline 220. Executions may be performed on-demand(e.g., based on user input) and/or automatically (e.g., based on aschedule).

The communication engine 210 may function to send requests, transmitand, receive communications, and/or otherwise provide communication withone or a plurality of systems. In some embodiments, the communicationengine 210 functions to encrypt and decrypt communications. Thecommunication engine 210 may function to send requests to and receivedata from one or more systems through a network or a portion of anetwork. Depending upon implementation-specific considerations, thecommunication engine 210 may send requests and receive data through aconnection, all or a portion of which may be a wireless connection. Thecommunication engine 210 may request and receive messages, and/or othercommunications from associated systems. Communications may be stored inthe dynamic pipeline visualization system datastore 212.

FIG. 3A-C depicts diagrams 300 of example dynamic visualizations of apipeline (e.g., a pipeline 220) according to some embodiments. In theexample of FIG. 3A, a first dynamic visualization (e.g., a default view)of a pipeline is generated and presented in a graphical user interfaceat a first level of granularity (e.g., a default level of granularity).The dynamic visualization of the pipeline includes nodes 320-334 andconnections 350-386. The pipeline includes various spatial relationships(e.g., distances between nodes), which may indicate a processing timefor operations, and/or the like, as discussed elsewhere herein. In theexample of FIG. 3B, a second dynamic visualization of the pipeline ispresented in the graphical user interface at a second level ofgranularity (e.g., a higher-level of granularity). More specifically,nodes 302-310 and connections 350-358 are shown in a relatively largerscale and include pipeline node information 302A-C, 304A, 306A-D,308A-C, 310A-B not present in the first dynamic visualization. Forexample, the pipeline node information (e.g., pipeline node information222) may include functions, datasets, and/or the like, as discussedelsewhere herein. In the example of FIG. 3B, the spatial relationships(e.g., relative distances between nodes, relative sizes of the nodes)are also maintained relative to the first dynamic visualization of FIG.3A.

In the example of FIG. 3C, a first pipeline schedule 390 and a secondpipeline schedule 392 are overlaid on a dynamic visualization of thepipeline. The pipeline schedules 390-392 are presented using differentline-weights for the connections between nodes, although it will beappreciated that other visual indicators may be used as well (e.g.,coloring, highlighting, and/or the like). The dashed-line represents aprotentional (e.g., recommended) pipeline 394 based on the existingfirst and second pipeline schedules 390-392 and/or other information(e.g., other pipeline schedules, context information, and/or the like).For example, rather than performing or scheduling a new pipelineexecution, which may be computationally expensive, the dynamic pipelinevisualization system 102 may suggest the potential pipeline 394 from aset of scheduled pipelines. For example, if a user requests to run orschedule a new pipeline execution, but a pipeline schedule alreadyexists for that pipeline, or that new pipeline execution includes pathsthat are already scheduled for execution in another pipeline schedulewithin a predetermined amount of time (e.g., it may not use pipelineschedules that are scheduled too far in advance of the requestedexecution time for the new pipeline), then the dynamic pipelinevisualization system 102 may present the protentional pipeline. In someembodiments, several different protentional pipelines may be generatedand/or presented, and the dynamic pipeline visualization system 102 mayindicate a processing time and/or other computing requirementsassociated with each potential pipeline (e.g., using visual indicators).This may allow, for example, a user to prioritize computationalefficiency over fresh data, or vice versa.

FIG. 4 depicts a flowchart 400 of an example of a method of generatingdynamic visualizations of a pipeline according to some embodiments. Inthis and other flowcharts, the flowchart 400 illustrates by way ofexample a sequence of steps. It should be understood the steps may bereorganized for parallel execution, or reordered, as applicable.Moreover, some steps that could have been included may have been removedto avoid providing too much information for the sake of clarity and somesteps that were included could be removed but may have been included forthe sake of illustrative clarity.

In step 402, a dynamic pipeline visualization system (e.g., dynamicpipeline visualization system 102) obtains a pipeline of operations(e.g., pipeline of operations 220). The pipeline of operations mayinclude a plurality of functions providing any of one or moremodification operations or visualization operations for a plurality ofdatasets. An example pipeline of operations is shown in FIGS. 3A-C. Insome embodiments, a management engine (e.g., management engine 202)obtains the pipeline of operations from a datastore (e.g., dynamicpipeline visualization system datastore 212).

In step 404, the dynamic pipeline visualization system generates a firstdynamic visualization of the pipeline of operations at a first level ofgranularity. An example is shown in FIG. 3A. In some embodiments, apipeline interaction engine (e.g., pipeline interaction engine 204)generates the first dynamic visualization of the pipeline of operationsat the first level of granularity (e.g., a default level ofgranularity). In some embodiments, the type and/or amount of informationassociated included in the dynamic visualization, and/or other dynamicvisualizations described herein, are determined based on context (e.g.,contextual information 224).

In step 406, the dynamic pipeline visualization system generates, inresponse to user input, a second dynamic visualization of the pipelineof operations at a second level of granularity. In one example, thesecond level of granularity may be a higher level of granularityrelative to the first level of granularity (e.g., a zoomed-in viewrelative the first dynamic visualization). An example of a zoomed-inview is shown in FIG. 3B. For example, the second dynamic visualizationmay present a portion of the pipeline of operations at a larger relativescale and/or present more information (e.g., more datasets/functions,larger labels/text, and/or the like) than the first dynamicvisualization.

In another example, the second level of granularity may be a lower levelof granularity relative to the first level of granularity (e.g., azoomed-out view relative the first dynamic visualization). For example,the second dynamic visualization may present a portion of the pipelineof operations at a smaller relative scale and/or present lessinformation (e.g., fewer datasets and/or functions, smaller labels/text,and/or the like) than the first dynamic visualization. An example of azoomed-in view is shown in FIG. 3B.

In some embodiments, the dynamic pipeline visualization system maygenerate any number of such dynamic visualizations of a pipeline ofoperations. For example, the pipeline interaction engine may generate athird dynamic visualization of the pipeline of operations at a thirdlevel of granularity (e.g., a level of granularity in between the firstand second levels of granularity, a level of granularity lower than thesecond level of granularity, and so forth).

In some embodiments, the dynamic pipeline visualization system maintainsthe same relative spatial relations for the various dynamicvisualizations of the pipeline of operations. For example, the first,second and third dynamic visualizations of the pipeline may have thesame relative distance between nodes of the pipeline operations,although they may each present different levels of granularity. In someembodiments, the spatial relationships may represent a relative orabsolute processing (e.g., execution) time for operations associatedwith those nodes.

In some embodiments, the dynamic pipeline visualization system mayobtain a plurality of pipeline schedules (e.g., pipeline schedules 228).Each of the pipeline schedules may indicate a time and/or frequency arespective pipeline of the plurality of pipeline schedules is to beexecuted. In some embodiments, the dynamic pipeline visualization systemmay overlay a respective visual indicator for each of the plurality ofpipeline schedules on various dynamic visualizations (e.g., the firstdynamic visualization of the pipeline of operations at the first levelof granularity, the second dynamic visualization of the pipeline ofoperations). Each of the respective visual indicators may identify acorresponding pipeline schedule of the plurality of pipeline schedules.

Hardware Implementation

FIG. 5 depicts a block diagram of an example of a computer system 500upon which any of the embodiments described herein may be implemented.The computer system 500 includes a bus 502 or other communicationmechanism for communicating information, one or more hardware processors504 coupled with bus 502 for processing information. Hardwareprocessor(s) 504 may be, for example, one or more general purposemicroprocessors.

The computer system 500 also includes a main memory 506, such as arandom access memory (RAM), cache and/or other dynamic storage devices,coupled to bus 502 for storing information and instructions to beexecuted by processor 504. Main memory 506 also may be used for storingtemporary variables or other intermediate information during executionof instructions to be executed by processor 504. Such instructions, whenstored in storage media accessible to processor 504, render computersystem 500 into a special-purpose machine that is customized to performthe operations specified in the instructions.

The computer system 500 further includes a read only memory (ROM) 508 orother static storage device coupled to bus 502 for storing staticinformation and instructions for processor 504. A storage device 510,such as a magnetic disk, optical disk, or USB thumb drive (Flash drive),etc., is provided and coupled to bus 502 for storing information andinstructions.

The computer system 500 may be coupled via bus 502 to a display 512,such as a cathode ray tube (CRT) or LCD display (or touch screen), fordisplaying information to a computer user. An input device 514,including alphanumeric and other keys, is coupled to bus 502 forcommunicating information and command selections to processor 504.Another type of user input device is cursor control 516, such as amouse, a trackball, or cursor direction keys for communicating directioninformation and command selections to processor 504 and for controllingcursor movement on display 512. This input device typically has twodegrees of freedom in two axes, a first axis (e.g., x) and a second axis(e.g., y), that allows the device to specify positions in a plane. Insome embodiments, the same direction information and command selectionsas cursor control may be implemented via receiving touches on a touchscreen without a cursor.

The computing system 500 may include a user interface module toimplement a GUI that may be stored in a mass storage device asexecutable software codes that are executed by the computing device(s).This and other modules may include, by way of example, components, suchas software components, object-oriented software components, classcomponents and task components, processes, functions, attributes,procedures, subroutines, segments of program code, drivers, firmware,microcode, circuitry, data, databases, data structures, tables, arrays,and variables.

In general, the word “module,” as used herein, refers to logic embodiedin hardware or firmware, or to a collection of software instructions,possibly having entry and exit points, written in a programminglanguage, such as, for example, Java, C or C++. A software module may becompiled and linked into an executable program, installed in a dynamiclink library, or may be written in an interpreted programming languagesuch as, for example, BASIC, Perl, or Python. It will be appreciatedthat software modules may be callable from other modules or fromthemselves, and/or may be invoked in response to detected events orinterrupts. Software modules configured for execution on computingdevices may be provided on a computer readable medium, such as a compactdisc, digital video disc, flash drive, magnetic disc, or any othertangible medium, or as a digital download (and may be originally storedin a compressed or installable format that requires installation,decompression or decryption prior to execution). Such software code maybe stored, partially or fully, on a memory device of the executingcomputing device, for execution by the computing device. Softwareinstructions may be embedded in firmware, such as an EPROM. It will befurther appreciated that hardware modules may be comprised of connectedlogic units, such as gates and flip-flops, and/or may be comprised ofprogrammable units, such as programmable gate arrays or processors. Themodules or computing device functionality described herein arepreferably implemented as software modules, but may be represented inhardware or firmware. Generally, the modules described herein refer tological modules that may be combined with other modules or divided intosub-modules despite their physical organization or storage.

The computer system 500 may implement the techniques described hereinusing customized hard-wired logic, one or more ASICs or FPGAs, firmwareand/or program logic which in combination with the computer systemcauses or programs computer system 500 to be a special-purpose machine.According to one embodiment, the techniques herein are performed bycomputer system 500 in response to processor(s) 504 executing one ormore sequences of one or more instructions contained in main memory 506.Such instructions may be read into main memory 506 from another storagemedium, such as storage device 510. Execution of the sequences ofinstructions contained in main memory 506 causes processor(s) 504 toperform the process steps described herein. In alternative embodiments,hard-wired circuitry may be used in place of or in combination withsoftware instructions.

The term “non-transitory media,” and similar terms, as used hereinrefers to any media that store data and/or instructions that cause amachine to operate in a specific fashion. Such non-transitory media maycomprise non-volatile media and/or volatile media. Non-volatile mediaincludes, for example, optical or magnetic disks, such as storage device510. Volatile media includes dynamic memory, such as main memory 506.Common forms of non-transitory media include, for example, a floppydisk, a flexible disk, hard disk, solid state drive, magnetic tape, orany other magnetic data storage medium, a CD-ROM, any other optical datastorage medium, any physical medium with patterns of holes, a RAM, aPROM, and EPROM, a FLASH-EPROM, NVRAM, any other memory chip orcartridge, and networked versions of the same.

Non-transitory media is distinct from but may be used in conjunctionwith transmission media. Transmission media participates in transferringinformation between non-transitory media. For example, transmissionmedia includes coaxial cables, copper wire and fiber optics, includingthe wires that comprise bus 502. Transmission media can also take theform of acoustic or light waves, such as those generated duringradio-wave and infra-red data communications.

Various forms of media may be involved in carrying one or more sequencesof one or more instructions to processor 504 for execution. For example,the instructions may initially be carried on a magnetic disk or solidstate drive of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over atelephone line using a modem. A modem local to computer system 500 canreceive the data on the telephone line and use an infra-red transmitterto convert the data to an infra-red signal. An infra-red detector canreceive the data carried in the infra-red signal and appropriatecircuitry can place the data on bus 502. Bus 502 carries the data tomain memory 506, from which processor 504 retrieves and executes theinstructions. The instructions received by main memory 506 may retrievesand executes the instructions. The instructions received by main memory506 may optionally be stored on storage device 510 either before orafter execution by processor 504.

The computer system 500 also includes a communication interface 518coupled to bus 502. Communication interface 518 provides a two-way datacommunication coupling to one or more network links that are connectedto one or more local networks. For example, communication interface 518may be an integrated services digital network (ISDN) card, cable modem,satellite modem, or a modem to provide a data communication connectionto a corresponding type of telephone line. As another example,communication interface 518 may be a local area network (LAN) card toprovide a data communication connection to a compatible LAN (or WANcomponent to communicated with a WAN). Wireless links may also beimplemented. In any such implementation, communication interface 518sends and receives electrical, electromagnetic or optical signals thatcarry digital data streams representing various types of information.

A network link typically provides data communication through one or morenetworks to other data devices. For example, a network link may providea connection through local network to a host computer or to dataequipment operated by an Internet Service Provider (ISP). The ISP inturn provides data communication services through the world wide packetdata communication network now commonly referred to as the “Internet”.Local network and Internet both use electrical, electromagnetic oroptical signals that carry digital data streams. The signals through thevarious networks and the signals on network link and throughcommunication interface 518, which carry the digital data to and fromcomputer system 500, are example forms of transmission media.

The computer system 500 can send messages and receive data, includingprogram code, through the network(s), network link and communicationinterface 518. In the Internet example, a server might transmit arequested code for an application program through the Internet, the ISP,the local network and the communication interface 518.

The received code may be executed by processor 504 as it is received,and/or stored in storage device 510, or other non-volatile storage forlater execution.

Engines, Components, and Logic

Certain embodiments are described herein as including logic or a numberof components, engines, or mechanisms. Engines may constitute eithersoftware engines (e.g., code embodied on a machine-readable medium) orhardware engines. A “hardware engine” is a tangible unit capable ofperforming certain operations and may be configured or arranged in acertain physical manner. In various example embodiments, one or morecomputer systems (e.g., a standalone computer system, a client computersystem, or a server computer system) or one or more hardware engines ofa computer system (e.g., a processor or a group of processors) may beconfigured by software (e.g., an application or application portion) asa hardware engine that operates to perform certain operations asdescribed herein.

In some embodiments, a hardware engine may be implemented mechanically,electronically, or any suitable combination thereof. For example, ahardware engine may include dedicated circuitry or logic that ispermanently configured to perform certain operations. For example, ahardware engine may be a special-purpose processor, such as aField-Programmable Gate Array (FPGA) or an Application SpecificIntegrated Circuit (ASIC). A hardware engine may also includeprogrammable logic or circuitry that is temporarily configured bysoftware to perform certain operations. For example, a hardware enginemay include software executed by a general-purpose processor or otherprogrammable processor. Once configured by such software, hardwareengines become specific machines (or specific components of a machine)uniquely tailored to perform the configured functions and are no longergeneral-purpose processors. It will be appreciated that the decision toimplement a hardware engine mechanically, in dedicated and permanentlyconfigured circuitry, or in temporarily configured circuitry (e.g.,configured by software) may be driven by cost and time considerations.

Accordingly, the phrase “hardware engine” should be understood toencompass a tangible entity, be that an entity that is physicallyconstructed, permanently configured (e.g., hardwired), or temporarilyconfigured (e.g., programmed) to operate in a certain manner or toperform certain operations described herein. As used herein,“hardware-implemented engine” refers to a hardware engine. Consideringembodiments in which hardware engines are temporarily configured (e.g.,programmed), each of the hardware engines need not be configured orinstantiated at any one instance in time. For example, where a hardwareengine comprises a general-purpose processor configured by software tobecome a special-purpose processor, the general-purpose processor may beconfigured as respectively different special-purpose processors (e.g.,comprising different hardware engines) at different times. Softwareaccordingly configures a particular processor or processors, forexample, to constitute a particular hardware engine at one instance oftime and to constitute a different hardware engine at a differentinstance of time.

Hardware engines can provide information to, and receive informationfrom, other hardware engines. Accordingly, the described hardwareengines may be regarded as being communicatively coupled. Where multiplehardware engines exist contemporaneously, communications may be achievedthrough signal transmission (e.g., over appropriate circuits and buses)between or among two or more of the hardware engines. In embodiments inwhich multiple hardware engines are configured or instantiated atdifferent times, communications between such hardware engines may beachieved, for example, through the storage and retrieval of informationin memory structures to which the multiple hardware engines have access.For example, one hardware engine may perform an operation and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware engine may then, at a latertime, access the memory device to retrieve and process the storedoutput. Hardware engines may also initiate communications with input oroutput devices, and can operate on a resource (e.g., a collection ofinformation).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented enginesthat operate to perform one or more operations or functions describedherein. As used herein, “processor-implemented engine” refers to ahardware engine implemented using one or more processors.

Similarly, the methods described herein may be at least partiallyprocessor-implemented, with a particular processor or processors beingan example of hardware. For example, at least some of the operations ofa method may be performed by one or more processors orprocessor-implemented engines. Moreover, the one or more processors mayalso operate to support performance of the relevant operations in a“cloud computing” environment or as a “software as a service” (SaaS).For example, at least some of the operations may be performed by a groupof computers (as examples of machines including processors), with theseoperations being accessible via a network (e.g., the Internet) and viaone or more appropriate interfaces (e.g., an Application ProgramInterface (API)).

The performance of certain of the operations may be distributed amongthe processors, not only residing within a single machine, but deployedacross a number of machines. In some example embodiments, the processorsor processor-implemented engines may be located in a single geographiclocation (e.g., within a home environment, an office environment, or aserver farm). In other example embodiments, the processors orprocessor-implemented engines may be distributed across a number ofgeographic locations.

Language

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Although an overview of the subject matter has been described withreference to specific example embodiments, various modifications andchanges may be made to these embodiments without departing from thebroader scope of embodiments of the present disclosure. Such embodimentsof the subject matter may be referred to herein, individually orcollectively, by the term “invention” merely for convenience and withoutintending to voluntarily limit the scope of this application to anysingle disclosure or concept if more than one is, in fact, disclosed.

The embodiments illustrated herein are described in sufficient detail toenable those skilled in the art to practice the teachings disclosed.Other embodiments may be used and derived therefrom, such thatstructural and logical substitutions and changes may be made withoutdeparting from the scope of this disclosure. The Detailed Description,therefore, is not to be taken in a limiting sense, and the scope ofvarious embodiments is defined only by the appended claims, along withthe full range of equivalents to which such claims are entitled.

It will be appreciated that an “engine,” “system,” “datastore,” and/or“database” may comprise software, hardware, firmware, and/or circuitry.In one example, one or more software programs comprising instructionscapable of being executable by a processor may perform one or more ofthe functions of the engines, datastores, databases, or systemsdescribed herein. In another example, circuitry may perform the same orsimilar functions. Alternative embodiments may comprise more, less, orfunctionally equivalent engines, systems, datastores, or databases, andstill be within the scope of present embodiments. For example, thefunctionality of the various systems, engines, datastores, and/ordatabases may be combined or divided differently.

The datastores described herein may be any suitable structure (e.g., anactive database, a relational database, a self-referential database, atable, a matrix, an array, a flat file, a documented-oriented storagesystem, a non-relational No-SQL system, and the like), and may becloud-based or otherwise.

As used herein, the term “or” may be construed in either an inclusive orexclusive sense. Moreover, plural instances may be provided forresources, operations, or structures described herein as a singleinstance. Additionally, boundaries between various resources,operations, engines, engines, and data stores are somewhat arbitrary,and particular operations are illustrated in a context of specificillustrative configurations. Other allocations of functionality areenvisioned and may fall within a scope of various embodiments of thepresent disclosure. In general, structures and functionality presentedas separate resources in the example configurations may be implementedas a combined structure or resource. Similarly, structures andfunctionality presented as a single resource may be implemented asseparate resources. These and other variations, modifications,additions, and improvements fall within a scope of embodiments of thepresent disclosure as represented by the appended claims. Thespecification and drawings are, accordingly, to be regarded in anillustrative rather than a restrictive sense.

Each of the processes, methods, and algorithms described in thepreceding sections may be embodied in, and fully or partially automatedby, code modules executed by one or more computer systems or computerprocessors comprising computer hardware. The processes and algorithmsmay be implemented partially or wholly in application-specificcircuitry.

The various features and processes described above may be usedindependently of one another, or may be combined in various ways. Allpossible combinations and sub-combinations are intended to fall withinthe scope of this disclosure. In addition, certain method or processblocks may be omitted in some implementations. The methods and processesdescribed herein are also not limited to any particular sequence, andthe blocks or states relating thereto can be performed in othersequences that are appropriate. For example, described blocks or statesmay be performed in an order other than that specifically disclosed, ormultiple blocks or states may be combined in a single block or state.The example blocks or states may be performed in serial, in parallel, orin some other manner. Blocks or states may be added to or removed fromthe disclosed example embodiments. The example systems and componentsdescribed herein may be configured differently than described. Forexample, elements may be added to, removed from, or rearranged comparedto the disclosed example embodiments.

Conditional language, such as, among others, “can,” “could,” “might,” or“may,” unless specifically stated otherwise, or otherwise understoodwithin the context as used, is generally intended to convey that certainembodiments include, while other embodiments do not include, certainfeatures, elements and/or steps. Thus, such conditional language is notgenerally intended to imply that features, elements and/or steps are inany way required for one or more embodiments or that one or moreembodiments necessarily include logic for deciding, with or without userinput or prompting, whether these features, elements and/or steps areincluded or are to be performed in any particular embodiment.

Any process descriptions, elements, or blocks in the flow diagramsdescribed herein and/or depicted in the attached figures should beunderstood as potentially representing modules, segments, or portions ofcode which include one or more executable instructions for implementingspecific logical functions or steps in the process. Alternateimplementations are included within the scope of the embodimentsdescribed herein in which elements or functions may be deleted, executedout of order from that shown or discussed, including substantiallyconcurrently or in reverse order, depending on the functionalityinvolved, as would be understood by those skilled in the art.

It should be emphasized that many variations and modifications may bemade to the above-described embodiments, the elements of which are to beunderstood as being among other acceptable examples. All suchmodifications and variations are intended to be included herein withinthe scope of this disclosure. The foregoing description details certainembodiments of the invention. It will be appreciated, however, that nomatter how detailed the foregoing appears in text, the invention can bepracticed in many ways. As is also stated above, it should be noted thatthe use of particular terminology when describing certain features oraspects of the invention should not be taken to imply that theterminology is being re-defined herein to be restricted to including anyspecific characteristics of the features or aspects of the inventionwith which that terminology is associated. The scope of the inventionshould therefore be construed in accordance with the appended claims andany equivalents thereof.

The invention claimed is:
 1. A system comprising: one or moreprocessors; and a memory storing instructions that, when executed by theone or more processors, cause the system to perform: obtaining apipeline of operations associated with datasets, the pipeline ofoperations comprising nodes connected by edges, each of the nodesincluding one or more modification operations to be performed on thedatasets and the edges indicating spatial distances between the nodes,the spatial distances indicating processing times of the one or moremodification operations; obtaining pipeline schedules, each of thepipeline schedules indicating a time or frequency a respective pipelineassociated with the pipeline schedules is to be executed; generating afirst dynamic visualization of the pipeline of operations; andoverlaying visual indicators for three pipeline portions associated withthe pipeline schedules on the first dynamic visualization, the visualindicators comprising: a first visual indicator highlighting a firstpipeline portion using a first line having a first line-weight; and asecond visual indicator highlighting a second pipeline portion using asecond line having a second line-weight; and a third visual indicatorhighlighting a potential pipeline on the first dynamic visualizationusing a third line having a different characteristic compared to thefirst line and the second line.
 2. The system of claim 1, wherein thefirst dynamic visualization of the pipeline of operations comprises adefault visualization of the pipeline of operations.
 3. The system ofclaim 1, wherein the instructions, when executed, further cause thesystem to perform: generating, in response to a user input, a seconddynamic visualization of the pipeline of operations at a second level ofgranularity, wherein the second dynamic visualization of the pipeline ofoperations comprises a zoomed-in view of a portion of the pipeline ofoperations relative to the first dynamic visualization of the pipelineof operations.
 4. The system of claim 3, wherein the second dynamicvisualization of the pipeline includes a representation of at least onedataset not represented in the first dynamic visualization of thepipeline of operations.
 5. The system of claim 1, wherein theinstructions, when executed, further cause the system to perform:generating a third dynamic visualization of the pipeline of operationsat a third level of granularity.
 6. The system of claim 5, wherein thethird dynamic visualization of the pipeline of operations comprises azoomed-out view of a portion of the pipeline of operations relative tothe second dynamic visualization of the pipeline of operations, andcomprises a zoomed-in view of the portion of the pipeline of operationsrelative to the first dynamic visualization of the pipeline ofoperations.
 7. The system of claim 5, wherein the third dynamicvisualization of the pipeline of operations includes a representation ofat least one data set not represented in the first dynamic visualizationof the pipeline of operations, and does not include at least one dataset represented in the second dynamic visualization of the pipeline ofoperations.
 8. The system of claim 3, wherein spatial relationships ofthe spatial distances are maintained between the first and seconddynamic visualizations of the pipeline of the operations.
 9. The systemof claim 1, wherein the spatial distances between the nodes representrelative processing times of the one or more modification operations.10. The system of claim 1, wherein the first dynamic visualizationincludes: a third visual indicator highlighting a potential pipelineusing a dashed line.
 11. The system of claim 1, wherein the instructionsfurther cause the system to perform: receiving an indication that anaccess privilege of an entity associated with the pipeline has changed;and in response to receiving the indication, modifying the pipeline bychanging the spatial distances between the nodes or adjusting theconnections between the nodes based on the change in the accessprivilege.
 12. A method being implemented by a computing systemincluding one or more physical processors and a storage media storingmachine-readable instructions, the method comprising: obtaining apipeline of operations associated with datasets, the pipeline ofoperations comprising nodes connected by edges, each of the nodesincluding one or more modification operations to be performed on thedatasets and the edges indicating spatial distances between the nodes,the spatial distances indicating processing times of the one or moremodification operations; obtaining pipeline schedules, each of thepipeline schedules indicating a time or frequency a respective pipelineassociated with the pipeline schedules is to be executed; generating afirst dynamic visualization of the pipeline of operations; andoverlaying visual indicators for three pipeline portions associated withthe pipeline schedules on the first dynamic visualization, the visualindicators comprising: a first visual indicator highlighting a firstpipeline portion using a first line having a first line-weight; and asecond visual indicator highlighting a second pipeline portion using asecond line having a second line-weight; and a third visual indicatorhighlighting a potential pipeline on the first dynamic visualizationusing a third line having a different characteristic compared to thefirst line and the second line.
 13. The method of claim 12, wherein thefirst dynamic visualization of the pipeline of operations comprises adefault visualization of the pipeline of operations.
 14. The method ofclaim 12, further comprising: generating, in response to a user input, asecond dynamic visualization of the pipeline of operations at a secondlevel of granularity, wherein the second dynamic visualization of thepipeline of operations comprises a zoomed-in view of a portion of thepipeline of operations relative to the first dynamic visualization ofthe pipeline of operations.
 15. The method of claim 14, wherein thesecond dynamic visualization of the pipeline includes a representationof at least one dataset not represented in the first dynamicvisualization of the pipeline of operations.
 16. The method of claim 15,further comprising generating a third dynamic visualization of thepipeline of operations, the third dynamic visualization comprising azoomed-out view of a portion of the pipeline of operations relative tothe second dynamic visualization of the pipeline of operations, andcomprises a zoomed-in view of the portion of the pipeline of operationsrelative to the first dynamic visualization of the pipeline ofoperations.
 17. The method of claim 16, wherein the third dynamicvisualization of the pipeline of operations includes a representation ofat least one data set not represented in the first dynamic visualizationof the pipeline of operations, and does not include at least one dataset represented in the second dynamic visualization of the pipeline ofoperations.
 18. The method of claim 14, wherein spatial relationships ofthe spatial distances are maintained between the first and seconddynamic visualizations of the pipeline of the operations.
 19. The methodof claim 12, wherein the spatial distances between operations nodesrepresent relative processing times of the one or more modificationoperations.
 20. A non-transitory memory storing machine-readableinstructions that, when executed by one or more processors of acomputing system, cause the computing system to perform: obtaining apipeline of operations associated with datasets, the pipeline ofoperations comprising nodes connected by edges, each of the nodesincluding one or more modification operations to be performed on thedatasets and the edges indicating spatial distances between the nodes,the spatial distances indicating processing times of the one or moremodification operations; obtaining pipeline schedules, each of thepipeline schedules indicating a time or frequency a respective pipelineassociated with the pipeline schedules is to be executed; generating afirst dynamic visualization of the pipeline of operations; andoverlaying visual indicators for three pipeline portions associated withthe pipeline schedules on the first dynamic visualization, the visualindicators comprising: a first visual indicator highlighting a firstpipeline portion using a first line having a first line-weight; and asecond visual indicator highlighting a second pipeline portion using asecond line having a second line-weight; and a third visual indicatorhighlighting a potential pipeline on the first dynamic visualizationusing a third line having a different characteristic compared to thefirst line and the second line.